1. Field of the Invention
The present invention is directed generally to a system and method for calculating changes in the energy efficiency of retail consumers of energy and reporting to those consumers and to other interested parties such as utilities, energy suppliers, regulators and other governmental agencies the results and ramifications of those changes.
2. Description of the Background
The cornerstone of an effective energy conservation program is the ability of the individual consumer to get a clear signal of the results of their energy conservation efforts and investments. For the vast majority of consumers, the only real measuring tool that signals the effect of their conservation efforts is their monthly utility bill. Their bill does not provide a clear signal due to changes in the weather and volatility in energy prices. Without clear feedback, consumers become less interested in attempting to control their energy usage, believing they have no control over their energy bill.
Only the largest consumers have been able to get a true understanding of the benefits of their conservation efforts through labor-intensive energy audits performed on a manual basis. Because of the high cost of these individual audits, it is not cost effective to perform them for retail consumers such as residential or small- to medium-sized commercial customers. The high cost of individual audits is driven by the need to manually process usage and weather data, individually deal with data deficiencies and to make manual adjustments for incomplete or inaccurate information. In manual audits, model selection occurs at the discretion of a human auditor, although there have been some attempts at automated model generation, such as the Prism approach, described in Fels, M., xe2x80x9cPRISM: An Introductionxe2x80x9d, Energy and Buildings, 9 (1986), pp. 5-18.
Utilities may develop a prediction of a consumer""s usage at xe2x80x9cnormalxe2x80x9d weather. Typically they do so by developing a linear fit between usage and weather and applying that fitted model to normalized weather. Those equations could be used in theory to calculate individual changes in energy efficiency. However, the accuracy of this method is not sufficient for these calculations. The Prism approach attempts to overcome this deficiency by the inclusion of a household specific variable tau. However, the Prism model effectively forces all households into the same equation structure of a linear regression. Prism also calculates a normal annual consumption in its determination of efficiency, and does not use the current weather condition to determine efficiency at that weather condition. The present invention chooses the optimal model structure for each consumer. The Prism approach develops a baseline and a non-baseline model for each consumer and exercises both models on normalized weather. The Prism approach is thus subject to numerous shortcomings including model inaccuracy far exceeding the change in normal consumption and errors caused by non-constant period lengths that can obscure the changes in efficiency.
An automated system and method for calculating changes in the energy efficiency of retail consumers of energy and reporting to those consumers the results and ramifications of those changes is needed such that the transaction cost of providing this information to retail consumers will be reduced such that it will be cost effective.
Using the present invention retail consumers can see the results of their behavioral changes such as resetting their thermostats, purchasing more energy efficient products such as sub-compact fluorescent light bulbs, high efficiency heating and cooling units and EnergyStar(copyright) compliant electronics and home-improvement projects such as installing additional insulation, stopping air leaks and installing storm doors and windows. Retail consumers will enjoy the same benefits currently available only to large commercial, governmental and industrial consumers through expensive, labor-intensive processes.
Efficiency changes in the context of the present invention are defined as changes to the whole consumption pattern related to a physical structure such as a house, apartment or commercial establishment. Efficiency includes considerations of equipment, consumer behavior and the physical aspects of the structure and space within.
Thus, there is a need for a system and method of measuring changes in energy efficiency for individual energy consumers at the retail customer level and reporting the results of that measurement to those consumers. In addition, there is a need for better reporting of individual and aggregate energy efficiency changes to utilities and third parties such as energy suppliers, regulators and other governmental agencies.
The present invention is directed to a method of receiving, processing and reporting data regarding retail consumers"" energy usage and weather in large batches. For each group of energy usage data to be processed, accurate historic weather data for multiple weather stations must be acquired. Weather data received may include Heating Degree Days, Cooling Degree Days, Relative Humidity, Dew point, Atmospheric Pressure and Precipitation. These data may be transmitted over a communication link such as, for example, the Internet, telephone lines or by computer readable media such as, for example, magnetic or optical storage media. Distribution can also be accomplished by distribution to a central storage site on the public Internet, an intranet, a local area network (LAN), a wide area network (WAN) or a direct connection for further access or distribution.
The data received for each weather station may be missing some data points or contain gross inaccuracies. The present invention detects missing or grossly inaccurate data points and correctly xe2x80x9cfillsxe2x80x9d the data using a variety of algorithms including but not limited to regressions, average replacements, deltas off of adjacent weather stations, deltas off of prior points, average prior and subsequent point and strategic estimates.
Each batch of energy usage data to be processed includes electronic data regarding anywhere from, for example, a few thousand to several million individual retail consumers. For each consumer, data regarding, for example, up to 24 months of usage is included. Data for each usage period for each consumer may include individual consumer record keys such as account number, sufficient location information to identify the appropriate weather station, consumption information including meter read data and dates, type of meter read which may include actual, estimate, correction and other billing information. In addition, contact information such as name, address and phone number may be included for reporting purposes.
The energy usage data (consumption in volumes of fuel such as gallons, MCF""s, and pounds, etc., or energy units such as kWh, therms, BTU""s, etc.) with related information about the periods of consumption such as starting date and number of days in a period, ending date and number of days in a period, starting and ending dates or a series of days in a period with a beginning or ending offset sufficient to determine the starting and ending dates of each consumption period received from, for example, the utility or energy supplier, may contain known data structure problems such as overlapping or missing meter read periods, invalid dates, such as February 30, invalid years such as 1901 appearing in a data set containing 2001 data, bad estimates, bad meter reads and accounting corrections, including those previously mentioned, and cancels and rebills. The present invention examines the data for these and other problems and repairs or removes problematic data elements using a variety of algorithms including but not limited to artificial intelligence, regression technology, analysis of variance, outlier analysis and human inspection.
The present invention examines the cleaned data for a baseline period and develops individual mathematical baseline models representing usage patterns for each consumer. The models may include but are not required to use or limited to the analysis of base non-weather related use, usage sensitive to changes in weather, temperatures at which the consumer turns on or off their heating and/or cooling systems, humidity, precipitation, wind speed, cloud cover, and trend variables.
The present invention then exercises the baseline models for each consumer using data representing actual weather that occurred in a period subsequent or prior to the baseline period to determine each consumer""s Actual Weather Consumption Estimate (AWCE). An alternate method is to develop a baseline and a non-baseline model for each consumer and to exercise both models over a consistent period of either actual or normalized weather.
Additional usage data for the individual consumers are received from, for example, the utility or energy supplier. These data correspond to the same period for which the AWCE has been calculated. Alternately these data may be received with the original data set.
The present invention then compares the AWCE to the actual usage in the same period to determine changes in the consumer""s energy usage patterns. The changes detected may be caused by changes in equipment, additions or removal of equipment such as replacing existing appliances with more efficient models or by consumer behavior changes such as changing thermostat settings, turning off unused equipment or limiting use of inefficient equipment.
The present invention prepares data to be reported to the consumer in an easy-to-understand format. Such information may include the quantity of energy used that is greater or less than the AWCE and the value of that energy usage in dollars. Additional information may be prepared including the environmental impact of the consumer""s energy efficiency change such as the pounds of emissions avoided or caused, the quantity of raw materials saved or used as a result of the consumer""s energy efficiency change and the societal impact such as the additional generation facilities needed or avoided by the energy efficiency change. This information is from average parameters related to the specific fuel consumed based on an industry, state or utility system. Further information may be provided to the consumer regarding recommendations for energy efficiency improvements and the potential benefit of those improvements.
Aggregate results at the utility, state or national level may also be included so the consumer may compare their personal results to those of a larger group.
The present invention includes a quality assurance method that randomly pulls a sample of individual consumer calculations to be compared to a hand calculation performed by the system operator or parallel operation performed by a second system. In addition, issues that are missed in the data cleanup may be determined at this stage. A system of prescribed reality checks using tests of known ratio ranges also are used to detect problems with modeling or data cleaning. The quality assurance step is important and should not be avoided. This step is crucial in establishing credibility with consumers and other interested parties such as regulators, legislators and utilities.
The present invention includes methods of communicating results of the energy efficiency change calculation to the individual consumers, the utility, and any other interested parties. These methods may include but are not limited to transmission of data over a communication link such as, for example, the Internet, public databases, telephone lines or by computer readable media such as, for example, magnetic or optical storage media. Distribution can also be accomplished by distribution to a central storage site on the public Internet, intranet, a local area network (LAN), a wide area network (WAN) or a direct connection for further access or distribution. Further communication methods to consumers may include direct mail, telephone communications, e-mail, personal visits, facsimile and public communications such as radio and TV broadcasting or billboards.
The message communicated to each consumer may take the form of an energy efficiency index, a percentage change in energy efficiency, actual quantity of energy efficiency variance, the dollar value of energy efficiency variance and values that may be inferred from the energy efficiency variance such as quantities or percentages of pollutants created or avoided. This information may be presented as a bar chart or a graph in addition to the stated numbers.